The Costs of Using AI to Manage Emotional Uncertainty (rashidazarang.com)

AI can be a powerful tool for turning emotional confusion into clear language and actionable perspective, but there’s a subtle risk when people habitually outsource early-stage emotional processing to models. The system’s ability to translate raw feeling into coherent narratives produces immediate relief — yet that relief can shortcut the somatic, time-dependent unfolding of uncertainty. Over repeated use this dynamic creates a competency gap: users get better at conceptualizing and narrating their lives but grow less practiced at tolerating unprocessed feeling, solitude, and the gritty ambiguity that builds emotional resilience and existential endurance. For the AI/ML community this is both an ethical and a design problem. Models used for reflection or therapy can unintentionally become “surrogate processors” that dampen interoception and dependency thresholds; metrics focused only on immediate user satisfaction or clarity miss long-term harms. Practically, product and research teams should consider safeguards such as friction or delay mechanisms, prompts that encourage embodied reflection (interoceptive exercises, time-to-wait), usage-monitoring for dependency, explicit boundary-setting, and collaboration with clinicians on evaluation frameworks that measure resilience and long-term well‑being, not just momentary coherence. Designing AI as a partner that scaffolds rather than substitutes for difficult internal work will help preserve the developmental experiences that no model can replicate.